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Privacy-Preserving Machine Learning for Speech Processing

:	Manas A. Pathak
:	Privacy-Preserving Machine Learning for Speech Processing
:	Springer-Verlag
:	9781461446392
:	1
:	CHF 89.50
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	142
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

This thesis discusses the privacy issues in speech-based applications such as biometric authentication, surveillance, and external speech processing services. Author Manas A. Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification and speech recognition. The author also introduces some of the tools from cryptography and machine learning and current techniques for improving the efficiency and scalability of the presented solutions. Experiments with prototype implementations of the solutions for execution time and accuracy on standardized speech datasets are also included in the text. Using the framework proposed may now make it possible for a surveillance agency to listen for a known terrorist without being able to hear conversation from non-targeted, innocent civilians.

Dr. Manas A. Pathak received the BTech degree in computer science from Visvesvaraya National Institute of Technology, Nagpur, India, in 2006, and the MS and PhD degrees from the Language Technologies Institute at Carnegie Mellon University (CMU) in 2009 and 2012 respectively. He is currently working as a research scientist at Adchemy, Inc. His research interests include intersection of data privacy, machine learning, speech processing.

	Privacy-PreservingMachine Learningfor Speech Processing	3
	Supervisor’s Foreword	6
	Acknowledgments	8
	Contents	10
	Acronyms	15
	Part I	15
	16	15
	1 Thesis Overview	17
	1.1 Motivation	17
	1.2 Thesis Statement	18
	1.3 Summary of Contributions	19
	1.4 Thesis Organization	20
	References	20
	2 Speech Processing Background	21
	2.1 Tools and Techniques	21
	2.1.1 Signal Parameterization	21
	2.1.2 Gaussian Mixture Models	22
	2.1.3 Hidden Markov Models	22
	2.2 Speaker Identification and Verification	24
	2.2.1 Modeling Speech	24
	2.2.2 Model Adaptation	26
	2.2.3 Supervectors with LSH	27
	2.2.4 Reconstructing Data from LSH Keys	29
	2.3 Speech Recognition	30
	References	31
	3 Privacy Background	33
	3.1 What is Privacy?	33
	3.1.1 Definitions	33
	3.1.2 Related Concepts	34
	3.1.3 Privacy-Preserving Applications	35
	3.1.4 Privacy-Preserving Computation in this Thesis	36
	3.2 Secure Multiparty Computation	36
	3.2.1 Protocol Assumptions	38
	3.2.2 Adversarial Behavior	39
	3.2.3 Privacy Definitions: Ideal Model and Real Model	40
	3.2.4 Encryption	41
	3.2.5 Masking	47
	3.2.6 Zero-Knowledge Proofs and Threshold Cryptosystems	49
	3.2.7 Oblivious Transfer	51
	3.2.8 Related Work on SMC Protocols for Machine Learning	53
	3.3 Differential Privacy	53
	3.3.1 Exponential Mechanism	55
	3.3.2 Related Work on Differentially Private Machine Learning	56
	3.3.3 Differentially Private Speech Processing	56
	References	57
	Part II	57
	60	57
	4 Overview of Speaker Verification with Privacy	61
	4.1 Introduction	61
	4.2 Privacy Issues and Adversarial Behavior	62
	4.2.1 Imposter Imitating a User	63
	4.2.2 Collusion	64
	4.2.3 Information Leakage After Multiple Interactions	64
	References	65
	5 Privacy-Preserving Speaker Verification Using Gaussian Mixture Models	66
	5.1 System Architecture	66
	5.2 Speaker Verification Protocols	68
	5.2.1 Private Enrollment Protocol	69
	5.2.2 Private Verification Protocols	69
	5.3 Experiments	71
	5.3.1 Precision	72
	5.3.2 Accuracy	72
	5.3.3 Execution Time	72
	5.4 Conclusion	73
	5.5 Supplementary Protocols	74
	References	77
	6 Privacy-Preserving Speaker Verification as String Comparison	78
	6.1 System Architecture	79
	6.2 Protocols	80
	6.3 Experiments	81
	6.3.1 Accuracy	81
	6.3.2 Execution Time	82
	6.4 Conclusion	83
	References	83
	Part III Privacy-Preserving Speaker Identification	84
	7 Overview of Speaker Identification with Privacy	85
	7.1 Introduction	85
	7.1.1 Speech-Based Surveillance	85
	7.1.2 Preliminary Step for Other Speech Processing Tasks	86
	7.2 Privacy Issues and Adversarial Behavior	87
	7.2.1 Collusion	88
	7.2.2 Information Leakage After Multiple Interactions	88
	8 Privacy-Preserving Speaker Identification Using Gaussian Mixture Models	89
	8.1 Introduction	89
	8.2 System Architecture	90
	8.3 Speaker Identification Protocols	91
	8.3.1 Case 1: Client Sends Encrypted Speech Sample to the Server	91
	8.3.2 Case 2: Server Sends Encrypted Speaker Models to the Client	93
	8.4 Experiments	95
	8.4.1 Precision	95
	8.4.2 Accuracy	95
	8.4.3 Execution Time	95
	8.5 Conclusion	96
	References	96
	9 Privacy-